Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	144,007
2	Rhode Island	143,012
3	South Dakota	140,193
4	Utah	126,289
5	Tennessee	124,010
6	Arizona	120,581
7	Iowa	117,548
8	Wisconsin	115,627
9	Nebraska	115,345
10	South Carolina	114,887
11	Oklahoma	114,324
12	New Jersey	114,226
13	Arkansas	112,835
14	Delaware	111,329
15	Alabama	110,655
16	Indiana	110,571
17	Illinois	108,997
18	Kansas	108,226
19	Florida	107,730
20	New York	107,714
21	Idaho	107,224
22	Mississippi	106,483
23	Minnesota	106,313
24	Nevada	104,871
25	Montana	104,305
26	Wyoming	103,445
27	Georgia	103,258
28	Kentucky	102,667
29	Massachusetts	102,371
30	Texas	101,642
31	Louisiana	100,982
32	Missouri	99,846
33	Michigan	98,915
34	Connecticut	97,297
35	New Mexico	96,529
36	California	95,662
37	North Carolina	95,283
38	Alaska	94,971
39	Colorado	94,163
40	Ohio	93,984
41	Pennsylvania	93,904
42	West Virginia	89,605
43	Virginia	78,935
44	Maryland	75,900
45	New Hampshire	72,388
46	District of Columbia	69,150
47	Washington	56,792
48	Puerto Rico	53,761
49	Maine	49,941
50	Oregon	47,278
51	Vermont	38,680
52	Hawaii	24,513

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	West Virginia	158
2	Colorado	136
3	Washington	129
4	Wyoming	120
5	Louisiana	116
6	Pennsylvania	105
7	Idaho	89
8	North Carolina	89
9	Florida	86
10	New Mexico	85
11	Oregon	83
12	Michigan	80
13	Rhode Island	76
14	Nevada	72
15	Illinois	71
16	Mississippi	71
17	Indiana	70
18	Kentucky	69
19	Texas	68
20	Minnesota	67
21	Alaska	66
22	Maine	66
23	Montana	66
24	Tennessee	61
25	North Dakota	59
26	Arizona	58
27	Utah	58
28	Ohio	56
29	South Carolina	56
30	Connecticut	53
31	Missouri	53
32	Arkansas	52
33	New York	51
34	Puerto Rico	49
35	Oklahoma	46
36	New Hampshire	45
37	Delaware	44
38	Alabama	43
39	Wisconsin	43
40	South Dakota	42
41	Georgia	41
42	Nebraska	41
43	Massachusetts	38
44	Iowa	37
45	Virginia	37
46	District of Columbia	35
47	Maryland	34
48	New Jersey	29
49	California	28
50	Kansas	28
51	Vermont	27
52	Hawaii	23

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,939
2	New York	2,705
3	Massachusetts	2,587
4	Rhode Island	2,555
5	Mississippi	2,452
6	Arizona	2,413
7	Connecticut	2,305
8	Alabama	2,268
9	Louisiana	2,268
10	South Dakota	2,265
11	Pennsylvania	2,119
12	New Mexico	2,028
13	Michigan	2,023
14	North Dakota	2,016
15	Indiana	2,015
16	Illinois	1,975
17	Arkansas	1,929
18	Iowa	1,914
19	Georgia	1,888
20	South Carolina	1,884
21	Nevada	1,806
22	Tennessee	1,801
23	Texas	1,774
24	Kansas	1,748
25	Oklahoma	1,748
26	Delaware	1,703
27	Florida	1,703
28	Ohio	1,689
29	District of Columbia	1,601
30	California	1,593
31	Missouri	1,567
32	West Virginia	1,552
33	Kentucky	1,541
34	Montana	1,502
35	Maryland	1,493
36	Wisconsin	1,332
37	Minnesota	1,325
38	Virginia	1,304
39	North Carolina	1,242
40	Wyoming	1,242
41	Nebraska	1,228
42	Idaho	1,168
43	Colorado	1,157
44	New Hampshire	988
45	Puerto Rico	776
46	Washington	757
47	Utah	714
48	Oregon	629
49	Maine	612
50	Alaska	481
51	Vermont	408
52	Hawaii	348

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	New Mexico	19
2	Wyoming	3
3	District of Columbia	2
4	Maryland	2
5	Michigan	2
6	Mississippi	2
7	Missouri	2
8	South Carolina	2
9	Arizona	1
10	Colorado	1
11	Florida	1
12	Georgia	1
13	Idaho	1
14	Illinois	1
15	Kentucky	1
16	Louisiana	1
17	Maine	1
18	Minnesota	1
19	Nevada	1
20	New Jersey	1
21	New York	1
22	North Carolina	1
23	Ohio	1
24	Pennsylvania	1
25	South Dakota	1
26	Virginia	1
27	West Virginia	1
28	Alabama	0
29	Alaska	0
30	Arkansas	0
31	California	0
32	Connecticut	0
33	Delaware	0
34	Hawaii	0
35	Indiana	0
36	Iowa	0
37	Kansas	0
38	Massachusetts	0
39	Montana	0
40	Nebraska	0
41	New Hampshire	0
42	North Dakota	0
43	Oklahoma	0
44	Oregon	0
45	Puerto Rico	0
46	Rhode Island	0
47	Tennessee	0
48	Texas	0
49	Utah	0
50	Vermont	0
51	Washington	0
52	Wisconsin	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	385,074	1	99
Crowley	Colorado	363,471	2	99
Bent	Colorado	276,134	3	99
Dewey	South Dakota	250,170	4	99
Lincoln	Arkansas	246,161	5	99
Davidson	Tennessee	142,875	190	93
Richland	South Carolina	113,193	980	68
York	South Carolina	112,855	995	68
Orange	California	85,564	2250	28
Pierce	Washington	59,579	2846	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,350	2	99
Jerauld	South Dakota	7,948	3	99
Hancock	Georgia	7,922	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,592	1834	41
York	South Carolina	1,370	2092	33
Richland	South Carolina	1,369	2093	33
Davidson	Tennessee	1,366	2097	33
Pierce	Washington	755	2739	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.